Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model.

INTRODUCTION: Machine learning techniques have shown excellent performance in 3D medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) utilizing SVS/STS-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between auTBAD patients based on rate of aortic growth. METHODS: Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two CT scans were analyzed: (1) the baseline CTA at index admission, and (2) either the most recent surveillance CTA, or the most recent CTA prior to an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase ≥5mm/year) and no/slow growth (diameter increase <5mm/year). Deidentified images were imported into an open-source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using 4-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study. RESULTS: Of 59 patients identified for inclusion, rapid growth was observed in 33 (56%) patients and no/slow growth was observed in 26 (44%) patients. There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (p>0.05 for all). Median duration between baseline and interval CT was 1.07 years (IQR 0.38-2.57). Post-discharge aortic intervention was performed in 13 (22%) of patients at a mean of 1.5±1.2 years, with no difference between groups (p>0.05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9% IQR -6.82-35.1) and zone 5 (13.4% IQR -7.78-37.9). There were no differences in baseline zone volumes between groups (p>0.05 for all). Average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91). CONCLUSIONS: We describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open-source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between rapid growth and no/slow growth patients.

The Incidence of Retained Objects in Intraoperative X-rays for Missing Counts in Plastic Surgery: We Should Do Better.

Background: In the event of incorrect surgical counts, obtaining X-rays to rule out retained surgical items (RSI) is standard practice. However, these safeguards also carry risk. This study investigates the actual incidence of RSI in plastic reconstructive surgery (PRS) cases as measured on intraoperative X-rays and its associated modifiable risk factors. Methods: X-rays with indication of "foreign body" in PRS procedures from 2012 to 2022 were obtained. Reports with "incorrect surgical counts" and associated perioperative records were retrospectively analyzed to determine the incidence of retained surgical items. Results: Among 257 X-rays, 21.4% indicated incorrect counts during PRS operations. None were positive for RSIs. The average number of staff present was 12.01. This correlated to an average of 6.98 staff turnovers. The average case lasted 8.42 hours. X-rays prolonged the time under anesthesia by an average of 24.3 minutes. Free flap surgery had 49.1% prevalence of missing counts (lower extremity 25.5%, breast 20%, craniofacial 3.6%), followed by hand (14.5%), breast (10.9%), abdominal reconstruction (10.9%), craniofacial (9.1%), and cosmetic (5.4%). Conclusions: Although X-rays for incorrect counts intend to prevent catastrophic sequela of inadvertent RSIs, our results suggest the true incidence of RSI in PRS is negligible. However, intraoperative X-rays have potentially detrimental and pervasive consequences for patients, including increased anesthesia time, radiation exposure, and higher overall cost. Addressing modifiable risk factors to minimize unnecessary intraoperative X-rays is imperative while also considering whether this modality is an effective and appropriate tool in PRS procedures with incorrect surgical counts.

Efficacy and safety of transarterial chemoembolization with 70- to 150-µm drug-eluting beads alone or in combination with 100- to 300-µm drug-eluting beads.

To evaluate the safety and efficacy of transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma, a retrospective analysis of patients who underwent TACE utilizing up to one vial of LC BeadM1 (70-150 µm) embolics with doxorubicin was performed. If tumor devascularization was not achieved, up to one additional vial of LC Bead (100-300 µm) embolics with doxorubicin was subsequently administered. Ninety-four sessions of TACE were completed among 48 patients. After a mean follow-up of 182 days, complete response was achieved in 10 patients (21%), partial response in 19 (40%), stable disease in 8 (17%), and progressive disease in 10 (21%). Disease control was achieved in 79% of patients: 90% of patients treated with 70- to 150-µm embolics alone vs. 70% of patients treated with both sizes of embolics (P = 0.47). Pathologic examination revealed complete necrosis in 5 of 10 tumors in eight transplants. Overall survival at 3, 12, and 24 months was 100%, 94%, and 94%, respectively. The median progression-free survival was 282 days. In conclusion, drug-eluting bead TACE with 70- to 150-µm embolics alone or in combination with 100- to 300-µm embolics is safe and demonstrates good disease control, particularly with small, solitary tumors.

Vascular lakes after doxorubicin drug-eluting bead chemoembolization with histologic analysis.

This brief case presentation addresses an imaging phenomenon sometimes seen during chemoembolization, referred to the literature as vascular lakes or vascular lakes phenomenon. Other papers have discussed the clinical significance of this imaging pattern. We present a case of vascular lakes seen at the time of chemoembolization, with associated histologic findings after liver explantation. To the authors' knowledge, this case represents the first histological correlative analysis of this imaging pattern.